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Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions

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Abstract

The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize people’s lifestyles across temporal scales. This includes long-term choices such as residential and work location choices that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that affect travel demand. Prior research in this area has been limited by the complexities associated with the development of integrated model systems that combine the long-, medium- and short-term choices into a unified analytical framework. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology. Model estimation results using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another, but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast the “true” causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions in an integrated framework.

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Notes

  1. Admittedly, we are limiting the discussion of integrated land-use transportation modeling to the interactions between the individual/household choices that influence land-use patterns and choices that influence travel demand patterns. In a broader sense, the term integrated land-use transportation modeling includes several other important aspects such as the interactions between individuals/households, and other players within the housing, labor, and transportation markets. The other players include real estate developers, employers, and production, manufacturing and service firms. The choices made by all the players in these markets and the demand–supply interactions in these markets influence the spatial structure of the land-use patterns and the travel demand patterns. In addition, the dynamics of the above-mentioned interactions gives rises to the evolution of the urban systems from one state to another. Equally important are the issues related to the evolution of socio-demographics and employment patterns. A truly integrated urban model would also consider the interaction of other urban amenities such as water and telecommunication networks with the land-use and travel demand patterns in the region.

  2. Examples of the nested logit approach to jointly model location and mobility choice include Abraham and Hunt (1997), Waddell (1993a), Ben-Akiva and de Palma (1986), and Eliasson and Mattsson (2000). The MNL and NL approaches are also at the heart of a series of papers by Anas and colleagues (Anas and Duann 1985; Anas 1995, 1981) that form the basis of an integrated land-use and transportation model.

  3. The nested logit model requires that the coefficient on the expected maximum utility of choice alternatives in a nest (this coefficient is labeled as the logsum parameter) should be between 0 and 1 (Ben-Akiva and Lerman 1985). Further, with more than two levels (e.g., residential location, auto ownership, and mode choice), the logsum parameters have to be in the ascending order from the bottom level nest to the top level nest. Due to such restrictions, it is difficult to estimate nested logit models with multi-level nested structures.

  4. For example, neither of the approaches offers a clear understanding of the extent of residential self-selection effects with respect to different land-use attributes. In the nested logit approach, the self-selection effect is estimated as the extent of correlations among unobserved factors (such as attitudes and travel preferences) affecting different travel choice alternatives. However, the self-selection effect is only a part of the correlations captured in a nested logit model of residential location and travel choices. Thus, the estimated self-selection effect may be confounded with several other unobserved factors, leading to potential overestimation of self-selection. Further, a common self-selection effect is estimated for all land-use attributes, without disentangling the extent of the effect with respect to each attribute. Thus, it is not possible to understand, for example, the difference in the extent of self-selection with respect to population density and bicycling facilities.

  5. The addition of the bicycle ownership dimension is important from a non-motorized travel behavior (such as bicycle use) analysis perspective, which is of considerable interest to the transportation planning profession. Further, bicycle ownership has been a relatively understudied variable.

  6. Admittedly, modeling the mode choice of only a single commuter per household does not consider all interdependencies between mode choice and other decisions (in Fig. 1) in multi-commuter households. For example, in a two commuter household, it is possible that one commuter’s work location and mode choice preferences influence the households’ residential location choice, while the other commuter may simply make the work location and mode choices conditional upon the residential location. Alternatively, both the commuters may have a certain degree of influence on the residential location. Such interdependencies between the mode choice preferences of different commuters and other household level choices in multi-commuter households cannot be captured in the current modeling framework.

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Acknowledgements

The authors appreciate the useful comments of four anonymous reviewers on an earlier manuscript.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of South Florida, 4202 E. Fowler Ave, ENB 118, Tampa, FL, USA

    Abdul Rawoof Pinjari

  2. School of Sustainable Engineering and the Built Environment, Arizona State University, Room ECG252, Tempe, AZ, 85287-5306, USA

    Ram M. Pendyala

  3. Department of Civil, Architectural & Environmental Engineering, The University of Texas at Austin, 1 University Station C1761, Austin, TX, 78712-0278, USA

    Chandra R. Bhat

  4. Department of City and Regional Planning, University of California, Berkeley, 228 Wurster Hall #1850, Berkeley, CA, 94720-1850, USA

    Paul A. Waddell

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  1. Abdul Rawoof Pinjari
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  2. Ram M. Pendyala
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Correspondence to Ram M. Pendyala.

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Pinjari, A.R., Pendyala, R.M., Bhat, C.R. et al. Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions. Transportation 38, 933–958 (2011). https://doi.org/10.1007/s11116-011-9360-y

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